# A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

arXiv: https://arxiv.org/abs/2306.11963 · HTML: https://mlsanity.com/paper/2306.11963/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2023.

## Built on by (most cited first, 25 of 25)

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- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
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- [Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding](https://mlsanity.com/paper/2506.13104/) (2025)
- [TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification](https://mlsanity.com/paper/2403.01802/) (2024)
- [Multi-modal Heart Failure Risk Estimation based on Short ECG and Sampled Long-Term HRV](https://mlsanity.com/paper/2403.15408/) (2024)
- [Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning](https://mlsanity.com/paper/2407.07277/) (2024)
- [Exploring Multi-Modality Dynamics: Insights and Challenges in Multimodal Fusion for Biomedical Tasks](https://mlsanity.com/paper/2411.00725/) (2024)
- [Challenges and proposed solutions in modeling multimodal medical data: A systematic review](https://mlsanity.com/paper/2505.06945/) (2025)
- [Enhanced Fracture Diagnosis Based on Critical Regional and Scale Aware in YOLO](https://mlsanity.com/paper/2509.23408/) (2025)
- [LENS-GRF: Permutation-Invariant Lesion Evidence Network with Gated Residual Fusion for Acne Severity Grading and Multi-Rater Clinical Oracle Analysis](https://mlsanity.com/paper/2610.00294/) (2026)
- [SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts](https://mlsanity.com/paper/2610.01418/) (2026)

## Abstract (arXiv)

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.